Artificial Neural Network Approach Assessment of Short-Circuit Fault Detection in a Three Phase Inverter

Abid, Mimouna; Laribi, Souad; Al-asgar, Zuhair S.; Larbi, M’hamed

doi:10.1109/icoten52080.2021.9493498

Cited by 4 publications

(3 citation statements)

References 6 publications

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“…This decrease is due to the variation in the nature and capacity of the PV field in terms of the amount of data and the number of faults detected. Moreover, the use of ANNs significantly reduced the risks associated with manual repairs and the time necessary for diagnosis when detecting reverse diode faults and partial shading of a photovoltaic module array from simulated data [86]. A technique based on fast Fourier transform and ANN was able to detect open-circuit and short-circuit faults in the 5-level cascaded inverter using the inverter's output voltage [87,88].…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

See 1 more Smart Citation

A Comprehensive Review of Supervised Learning Algorithms for the Diagnosis of Photovoltaic Systems, Proposing a New Approach Using an Ensemble Learning Algorithm

Toche Tchio,

Kenfack,

Kassegne

et al. 2024

Applied Sciences

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Photovoltaic systems are prone to breaking down due to harsh conditions. To improve the reliability of these systems, diagnostic methods using Machine Learning (ML) have been developed. However, many publications only focus on specific AI models without disclosing the type of learning used. In this article, we propose a supervised learning algorithm that can detect and classify PV system defects. We delve into the world of supervised learning-based machine learning and its application in detecting and classifying defects in photovoltaic (PV) systems. We explore the various types of faults that can occur in a PV system and provide a concise overview of the most commonly used machine learning and supervised learning techniques in diagnosing such systems. Additionally, we introduce a novel classifier known as Extra Trees or Extremely Randomized Trees as a speedy diagnostic approach for PV systems. Although this algorithm has not yet been explored in the realm of fault detection and classification for photovoltaic installations, it is highly recommended due to its remarkable precision, minimal variance, and efficient processing. The purpose of this article is to assist technicians, engineers, and researchers in identifying typical faults that are responsible for PV system failures, as well as creating effective control and supervision techniques that can minimize breakdowns and ensure the longevity of installed systems.

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Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

“…In a review of the application of ANNs in the diagnosis of PV systems, Li et al emphasized the importance of ANNs in the field of solar photovoltaics [21]. Finally, a new approach to diagnosing short-circuits faults and disconnected strings based on ANNs in a PV system was found to have an accuracy of 98.6% [86].…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

A Comprehensive Review of Supervised Learning Algorithms for the Diagnosis of Photovoltaic Systems, Proposing a New Approach Using an Ensemble Learning Algorithm

Toche Tchio,

Kenfack,

Kassegne

et al. 2024

Applied Sciences

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“…До них належать зовнішні та внутрішні короткі замикання в АІН, провали та відновлення напруги живлення та неповнофазні режими роботи АІН. Дослідження [6,7]…”

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Analysis of Emergency Modes of Traction Induction Electric Drive

Panchenko¹,

Panchenko²,

Turenko³

2023

ЗНПУкрДУЗТ

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The operation process of the traction asynchronous electric drive of the rolling stock may be accompanied by the occurrence of emergency modes, which are characterized by current overloads and may lead to damaging power electric circuits or mechanical part of the electric drive. Therefore, studying electromagnetic processes that occur in emergency modes of operation is relevant, in particular, for developing systems aimed at diagnosing and protecting traction equipment. Ukrainian and foreign studies of emergency modes in an asynchronous electric drive were analyzed. The most frequent causes of emergency modes are failures in static converters, therefore it is advisable to conduct an analysis of electromagnetic processes specifically in autonomous inverters. Using Matlab/Simulink software environment a simulation model of the vector control system of the traction asynchronous electric drive with spatial vector pulse width modulation was developed, and the short-circuit modes of the power switch of the autonomous voltage inverter and the disappearance of the supply voltage were simulated. The oscillograms of electromagnetic processes in these modes were shown and compared with the nominal mode of operation. The simulation results show that the analyzed emergency modes, even with a short duration of less than 0.5 s, can lead to significant damage to the electric drive. Both cases lead to significant current overloads: in the case of a short circuit of the power switch, a three-fold excess of the phase current is observed, during recovery after a supply voltage failure, the phase currents increase to the starting level, as well as an almost instantaneous increase in the supply voltage on the input filter capacitor occurs. The Fourier analysis of the phase A current in the above emergency modes shows an increase in the coefficient of nonlinear distortions by 65 %, a decrease in the amplitude of the main harmonic to 60 %, and the appearance of a significant number of subharmonics. In the case of a short circuit, the amplitudes of the subharmonics even exceed the fundamental harmonic. In addition, the process of returning from the emergency mode to normal at the first moment of time was characterized by a reduced moment of resistance (20 % of the nominal), and then by an exit to the nominal parameters.

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Open Circuit (OC) and Short Circuit (SC) IGBT Switch Fault Detection in Three-Phase Standalone Photovoltaic Inverters Using Shallow Neural Networks

Chand

Kumar

Prasad

et al. 2022

2022 25th International Conference on Electrical Machines and Systems (ICEMS)

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Artificial Neural Network Approach Assessment of Short-Circuit Fault Detection in a Three Phase Inverter

Cited by 4 publications

References 6 publications

A Comprehensive Review of Supervised Learning Algorithms for the Diagnosis of Photovoltaic Systems, Proposing a New Approach Using an Ensemble Learning Algorithm

A Comprehensive Review of Supervised Learning Algorithms for the Diagnosis of Photovoltaic Systems, Proposing a New Approach Using an Ensemble Learning Algorithm

Analysis of Emergency Modes of Traction Induction Electric Drive

Open Circuit (OC) and Short Circuit (SC) IGBT Switch Fault Detection in Three-Phase Standalone Photovoltaic Inverters Using Shallow Neural Networks

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